Accurate multi-source forest species mapping using the multiple spectral–spatial classification approach

Dimitris Stavrakoudis; Ioannis Gitas; Christos Karydas; Polychronis Kolokoussis; Vassilia Karathanassi

doi:10.1117/12.2194663

15 October 2015 Accurate multi-source forest species mapping using the multiple spectral–spatial classification approach

Dimitris Stavrakoudis, Ioannis Gitas, Christos Karydas, Polychronis Kolokoussis, Vassilia Karathanassi

Author Affiliations +

Proceedings Volume 9643, Image and Signal Processing for Remote Sensing XXI; 964324 (2015) https://doi.org/10.1117/12.2194663
Event: SPIE Remote Sensing, 2015, Toulouse, France

Abstract

This paper proposes an efficient methodology for combining multiple remotely sensed imagery, in order to increase the classification accuracy in complex forest species mapping tasks. The proposed scheme follows a decision fusion approach, whereby each image is first classified separately by means of a pixel-wise Fuzzy-Output Support Vector Machine (FO-SVM) classifier. Subsequently, the multiple results are fused according to the so-called multiple spectral– spatial classifier using the minimum spanning forest (MSSC-MSF) approach, which constitutes an effective post-regularization procedure for enhancing the result of a single pixel-based classification. For this purpose, the original MSSC-MSF has been extended in order to handle multiple classifications. In particular, the fuzzy outputs of the pixel-based classifiers are stacked and used to grow the MSF, whereas the markers are also determined considering both classifications. The proposed methodology has been tested on a challenging forest species mapping task in northern Greece, considering a multispectral (GeoEye) and a hyper-spectral (CASI) image. The pixel-wise classifications resulted in overall accuracies (OA) of 68.71% for the GeoEye and 77.95% for the CASI images, respectively. Both of them are characterized by high levels of speckle noise. Applying the proposed multi-source MSSC-MSF fusion, the OA climbs to 90.86%, which is attributed both to the ability of MSSC-MSF to tackle the salt-and-pepper effect, as well as the fact that the fusion approach exploits the relative advantages of both information sources.

Citation Download Citation

Dimitris Stavrakoudis, Ioannis Gitas, Christos Karydas, Polychronis Kolokoussis, and Vassilia Karathanassi "Accurate multi-source forest species mapping using the multiple spectral–spatial classification approach", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 964324 (15 October 2015); https://doi.org/10.1117/12.2194663

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image fusion

Image segmentation

Image classification

Fuzzy logic

Hyperspectral imaging

Image processing algorithms and systems

Spatial resolution

Show All Keywords

Keywords/Phrases

Search In:

Publication Years